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Collective intelligence evolution using ant colony optimization and neural networks

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Abstract

Recently, theory of collective intelligence (CI) evolution is proposed as a meta algorithm toward artificial general intelligence. But the only implementation of the CI algorithm of the theory is the Monte Carlo tree search (MCTS) used by AlphaZero. Since ant colony optimization (ACO) is an extensively used CI algorithm, it is useful to implement CI evolution using ACO. A genetic version of ACO is adapted to satisfy the CI evolution theory by two methods. One method is realized by using a policy network, namely policy network guided ACO (P-ACO). The other method is realized by using a policy network and a value network, namely policy and value network guided ACO (PV-ACO). Both methods of ACO evolution algorithm are applied to Tic-Tac-Toe and Four in a Row, where traditional ACO played poorly compared to the tree search algorithm, e.g., MCTS. Computational experiments are done to compare both methods with pure ACO and MCTS. As a result, the intelligence level of ACO evolution algorithm quickly exceeds pure ACO and MCTS. In this article, the performance of ACO evolution algorithm is analyzed and the feasibility of applying the CI evolution theory to a specific application is verified.

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Acknowledgements

The authors would like to thank Ji Hua Laboratory for the support of this paper (project ID X190021TB190). This study was also supported by Shanghai Engineering Research Center of AI and Robotics, Fudan University, China and Engineering Research Center of AI and Robotics, Ministry of Education, China.

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Authors and Affiliations

  1. Ji Hua Laboratory, Foshan, Guangdong Province, China

    Xiaoya Qi, Zhongxue Gan, Chuang Liu, Xiaozhi Zhang, Wei Li & Chun Ouyang

  2. Beijing Deep Singularity Technology Co., Ltd., Beijing, China

    Xiaoya Qi, Zhongxue Gan, Chuang Liu, Zheng Xu & Xiaozhi Zhang

  3. Shanghai Engineering Research Center of AI and Robotics, Shanghai, China

    Xiaoya Qi, Zhongxue Gan, Wei Li & Chun Ouyang

  4. Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China

    Xiaoya Qi, Zhongxue Gan, Wei Li & Chun Ouyang

  5. Institute of AI and Robotics, Fudan University, Shanghai, China

    Xiaoya Qi, Zhongxue Gan, Wei Li & Chun Ouyang

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  1. Xiaoya Qi
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  2. Zhongxue Gan
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Correspondence to Zhongxue Gan.

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Appendix

Appendix

1.1 Neural network architecture

The neural network architecture is a simplified version compared to that of AlphaZero. The input to the neural network is an \(n\times n \times 4\) image stack comprising 4 binary feature planes, where n denotes the size of the board. The first two feature planes: \(X_t\) and \(Y_t\), consist of binary values indicating the presence of current player’s pieces and opponent’s pieces respectively, where \(X_t^i = 1\) if intersection i contains a stone of current player at time step t, and \(Y_t^i = 1\) if intersection i contains a stone of opponent player; \(X_t^i, Y_t^i = 0\) if the intersection i is empty. The third feature plane is the latest move of opponent, the value of position corresponding to opponent’s latest move is 1 and others are 0. The final feature plane, C, denotes the color of the player who is to play, and has a constant value of either 1 if black is to play or 0 if white is to play. These planes are concatenated together to provide input features \(s_t=[X_t, Y_t, M_{t-1} ,C] \). History feature of the opponent’s latest move \(M_{t-1}\) and the color feature C are unnecessary in the games like Four in a Row, because the current board has perfect information for determining optimal policy and value function. Nonetheless, it does make sense that current player tends to place next move around the latest move position of its opponent, which denotes that history feature \(M_{t-1}\) can be instructive for current player. Similarly, the color feature C contains information whether current player is on the offensive and tends to win.

The input features \(s_t\) are proceeded by a public convolutional block followed by two separated fully connected networks. The public convolutional block uses the following modules:

  1. (1)

    A convolution of 32 filters of kernel size \(3\times 3\) with stride 1 and padding 1, which is activated by a rectifier nonlinearity;

  2. (2)

    A convolution of 64 filters of kernel size \(3\times 3\) with stride 1 and padding 1, which is activated by a rectifier nonlinearity;

  3. (3)

    A convolution of 128 filters of kernel size \(3\times 3\) with stride 1 and padding 1, which is activated by a rectifier nonlinearity.

The output of the public convolutional block is sent to two separate heads for computing the policy and value. The policy head applies the following modules:

  1. (1)

    A convolution of four filters of kernel size \(1\times 1\) with stride 1, which is activated by a rectifier nonlinearity;

  2. (2)

    A fully connected linear layer that outputs a vector with size \(n^2\);

  3. (3)

    A soft-max nonlinear function outputting possibilities of all positions, where the illegal moves are manually removed during playing and the probabilities are re-normalized.

The value head applies the following modules:

  1. (1)

    A convolution of 2 filters of kernel size \(1\times 1\) with stride 1, which is activated by a rectifier nonlinearity;

  2. (2)

    A fully connected linear layer of size 64, which is activated by a rectifier nonlinearity;

  3. (3)

    A fully connected linear layer to a scalar;

  4. (4)

    A tanh nonlinearity outputting a scalar in the range \([-1,1]\).

The overall network has a depth of 5 or 6, consisting of 3 public convolutional layers, plus 2 layers for the policy head and 3 layers for the value head. With this simplified network, training and predicting are relatively fast.

1.2 Sensitivity of the ant number

Since the number of ants is a critical parameter for the conventional ACO, here we perform simple experiments to analyze the sensitivity of the proposed algorithm. The experiments are performed by using a toy navigation task—grid-based path planning. The conventional ACO and the policy neural network guided ACO have been tested on a map of \(10\times 10\) grids with randomly generated start and target states. The average lengths of the trajectories over 20 trials are presented in Fig. 8. By comparing the two algorithms, it is clearly shown that for the conventional ACO, a greater number of ants leads to a quicker convergence. For the proposed method, apparently, there are differences between the one-ant case and the others in Fig. 8b. However, by comparing the length of cases for 2, 4 and 8 ants, the whole iteration number is not sensitive to the number of ants. It could be concluded that more ants (\(>2\)) may not improve the performance significantly but only increase the total amount of computation, which shows we do not need as many ants as the conventional ACO. The reason is that the conventional ACO uses more ants to explore the feasible space to avoid being trapped by a local optimum, while in our method, the exploration can also be driven by the stochasticity of the neural network output, and thus fewer ants are needed through the iterations of searching in the planning space. In other words, the solution is searched by many ants (for conventional ACO) or by fewer ants recursively iterated again and again.

Fig. 8
figure 8

Convergence analysis of different number of ants by a toy navigation task—grid-based path planing using two different algorithms: a the conventional ACO, b the policy neural network guided ACO

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Qi, X., Gan, Z., Liu, C. et al. Collective intelligence evolution using ant colony optimization and neural networks. Neural Comput & Applic 33, 12721–12735 (2021). https://doi.org/10.1007/s00521-021-05918-7

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